With the proliferation of chemical, biological, radiological and nuclear weapons, it is critical to be able to rapidly deploy equipment in the field to detect the release of these weapons for protection of military and civilian personnel.
Analysts require decision support tools that can assist them in planning for protection from these threats. These tools need to support sensor optimization in terms of their placement and sensor type mix (point and standoff sensors). To this end, efforts are being made to provide computer simulations and models of movement of a hazard over a three dimensional terrain based on the type of threat, weather and terrain conditions. Sensor location one of the more important factors in determining the efficacy of a particular sensor network in providing sufficient warning time and/or area coverage. There are numerous techniques to determine sensor location. One approach is to use advanced hazard environment modeling techniques coupled with sensor performance modeling routines and optimization algorithms. Hazard modeling techniques predict movement of a hazard based on the particular agent, weather and terrain in order to determine the nature, spatial extent and duration of the hazard. Sensor modeling techniques produce data representing a sensor's performance (will it detect or not) based on its ability to respond to the threat at various concentration thresholds. The optimization routine used to select the sensor location may include exhaustive searches and probabilistic models that allow determination of the optimal sensor layout given a range of threats and other criteria.
Computer modeling and simulation approaches heretofore known are too slow in producing results for sensor layouts. Traditional approaches require significant computational time and resources in order to generate data representing a four dimensional hazard environment. For each proposed or candidate location of a sensor or sensors, of which there may be hundreds or more, the conventional approach involves recalculating the hazard environment in order to determine whether and how fast a sensor layout will detect the hazard. For example, for a hazard scenario of 5 threats and 10 sensors, 50 hazard simulations or runs would be needed taking tens of hours of computing resources. There may be a much larger number of possible threats and sensor layouts to the point that the time required to determine an optimum sensor layout would not be able meet certain real-world scenarios.
Consequently, a method is needed to more rapidly determine optimum sensor locations in a hazard environment.